The formulated problem is particularly significant in the field of underwater robotics, where very little is known on the environment, and where the installation of external systems of positioning is expensive: installation and callibration for each new site of mission, asking heavy operational means. We can affirm that the cost of these systems of support, and the risk to lose the platform during the mission are the two major factors which return the systems autonomous excessively expensive compared with traditional means of intervention, thus slowing down their use for practical applications. The SAM project adopted like area of reference underwater robotics. However, the developed techniques of navigation can also be applied in many other fields such as agriculture, industrial cleaning, the mine clearance, etc.

The stated problems are studied in the project within a multidisciplinary framework:
 

• Signal Processing: construction, starting from the data coming from all the sensors installed on the robot, from a simplified, but correct and sufficient representation, of workspace (problem of coding); to evaluate correctly the autonomy of the robot at every moment (characterization of uncertainty); to effectively use acquired information (problem of fusion of data).
• Optimization : the definition of strategies for the acquisition of new information, and the planning of the trajectory of the robot for the effective observation of a given area, under conditions of strong uncertainty, lead to problems of multivariable optimization, for which the standard techniques of optimization are not adapted. The SAM project studies the application of algorithms of stochastic optimization (genetic algorithms) to these problems.
• Control : the project studies the problem of order under uncertainty (robust order), on all the levels of the architecture of control: order actuators, generation of plans, controls éxécution.